Local Adaptive Multiplicative Error Models for High-Frequency Forecasts
提出一种局部自适应乘法误差模型，通过序贯检验动态估计参数并选择最优局部窗口长度，在2008年五只纳斯达克股票1分钟累计交易量数据上，该模型显著优于固定窗口的MEM。
We propose a local adaptive multiplicative error model (MEM) accommodating time-varying parameters. MEM parameters are adaptively estimated based on a sequential testing procedure. A data-driven optimal length of local windows is selected, yielding adaptive forecasts at each point in time. Analysing 1-minute cumulative trading volumes of five large NASDAQ stocks in 2008, we show that local windows of approximately 3 to 4 hours are reasonable to capture parameter variations while balancing modelling bias and estimation (in)efficiency. In forecasting, the proposed adaptive approach significantly outperforms a MEM where local estimation windows are fixed on an ad hoc basis. Copyright © 2014 John Wiley & Sons, Ltd.